The electroencephalogram (EEG) conveys information related to different sleep processes. One of these processes is the Cyclic Alternating Pattern (CAP), which is correlated with sleep instability. CAP is composed of A-phases, which are short recurrent modifications to the EEG fluctuations that characterize the sleep stages. A-phase annotation is performed by trained clinicians by visual EEG inspection, thus this is a weary and time-consuming task. A-phase annotation is a three step task: 1) localization, 2) delineation and 3) categorization. We propose to resolve the first step, to identify the A-phase location by training a deep convolutional neural network (CNN) based on the A-phase clinical description: an abrupt modification of the basal EEG fluctuations. Whole night EEG recordings of nine healthy subjects were used in this study. As first step, a CNN was trained and tested with the Leave-One-Out scheme in a balanced dataset of 4s EEG segments where an A-phase onset was or was not present. As a second step, the trained CNNs were used to identify A-phase onsets across the whole night recording. The results showed an accuracy performance of 93%, sensitivity of 94% and specificity of 91% for the balanced set. On the whole recording, the performance was: F-score of 58%, recall of 70% and precision of 49%. In conclusion, we present a simple fully automatic method to localize the onset of A-phases in EEG signals. It is based on the spectral characteristics of the EEG signal which define the A-phases and could be part of more complex systems

Automatic detection of A-phase onsets based on convolutional neural networks

Bianchi, Anna M.
2022

Abstract

The electroencephalogram (EEG) conveys information related to different sleep processes. One of these processes is the Cyclic Alternating Pattern (CAP), which is correlated with sleep instability. CAP is composed of A-phases, which are short recurrent modifications to the EEG fluctuations that characterize the sleep stages. A-phase annotation is performed by trained clinicians by visual EEG inspection, thus this is a weary and time-consuming task. A-phase annotation is a three step task: 1) localization, 2) delineation and 3) categorization. We propose to resolve the first step, to identify the A-phase location by training a deep convolutional neural network (CNN) based on the A-phase clinical description: an abrupt modification of the basal EEG fluctuations. Whole night EEG recordings of nine healthy subjects were used in this study. As first step, a CNN was trained and tested with the Leave-One-Out scheme in a balanced dataset of 4s EEG segments where an A-phase onset was or was not present. As a second step, the trained CNNs were used to identify A-phase onsets across the whole night recording. The results showed an accuracy performance of 93%, sensitivity of 94% and specificity of 91% for the balanced set. On the whole recording, the performance was: F-score of 58%, recall of 70% and precision of 49%. In conclusion, we present a simple fully automatic method to localize the onset of A-phases in EEG signals. It is based on the spectral characteristics of the EEG signal which define the A-phases and could be part of more complex systems
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Convolutional neural networks, Deep learning, A-Phases, Cyclic alternating pattern, NREM sleep
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1216246
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